A Substation Fire Situation Awareness Algorithm Based on STM32 and AHP-CRITIC-EWM Combined Weighting Method

Once a fire accident occurs in a substation, which is the core hub of the power system, it will cause serious damage to social production and life. In unmanned intelligent substations, traditional monitoring methods are difficult to achieve rapid and accurate perception of the fire situation. This study designs a substation fire situation awareness algorithm based on STM32 and AHP-CRITIC-EWM combined weighting method. By fusing environmental data through a multi-sensor detection system and optimizing the weight distribution mechanism using the combined weighting method, the reliability and robustness of the algorithm in complex environments are improved, and an intelligent fire situation awareness system is constructed. The experimental results show that on the substation fire dataset, this algorithm can effectively achieve fire identification, demonstrating an extremely high recall rate and an extremely low false alarm rate. It has also achieved a relatively high accuracy rate in the task of dynamic hierarchical assessment of the fire situation. The research results of this study provide reliable quantitative decision support for the emergency response to fires in intelligent substations and have important engineering application value.